The Corporate Sustainability Due Diligence Directive (CSDDD) by the European Union (EU) mandates companies to identify and address environmental impacts and human rights issues within their supply chains. Procurement organizations play a pivotal role in both the effective implementation of and ongoing compliance with the CSDDD. This paper investigates which challenges procurement organizations must overcome along the stages of implementation through a Systematic Literature Review (SLR). The results indicate that the largest number of challenges arise during initial implementation, mainly related to communication and collaboration with suppliers as well as their willingness and ability to share data. Moreover, it highlights that substantial efforts will be required to ensure ongoing compliance. The results contribute to a systematic understanding of challenges imposed on procurement organizations and lay the groundwork for future research on potential solutions.
Recent advances in Large Language Models (LLMs) have enabled new opportunities in Computer-Aided Design (CAD) through natural language-driven code generation. We present Prompt2CAD, a lightweight and replicable framework that combines prompt-based macro generation with optional visual, human-in-the-loop refinement. Users provide a natural language description of a 3D part, and a general-purpose LLM (GPT-4o in this study) interprets and generates executable CAD scripts. If the output is incorrect, users can submit screenshots of the rendered CAD output and clarifying feedback for iterative correction—without requiring fine-tuning, scoring mechanisms, or additional Artificial Intelligence (AI) models. We evaluate Prompt2CAD on 63 modeling tasks of varying complexity. The framework achieves a 90.48% overall success rate, with 77.78% success on the first attempt and 57.14% resolved through iterative visual feedback. These results highlight the potential of general-purpose LLMs as collaborative design partners in CAD—bridging the gap between code automation and human-guided refinement.
In line with the Operator 5.0 vision of a resilient, human-centric workforce, this study presents a Virtual Reality (VR) training framework for reskilling and upskilling industrial operators in manual assembly tasks. The system is designed as a modular and immersive learning environment where users engage in hands-on procedural training using intuitive hand tracking and step-by-step guidance. The framework aims to reduce training time, improve task readiness, and enable safe, repeatable learning experiences without disrupting physical workflows. To explore the feasibility of the approach, the VR environment was validated using a standardized furniture assembly scenario based on the Human-Robot Collaboration (HRC) Model Set as a reproducible testbed for training transfer. Preliminary results from pilot sessions suggest that users trained in VR were able to assemble physical furniture components more confidently and accurately. Although full experimental trials are pending, this exploratory work demonstrates the viability and educational potential of VR-based training for operator skill development. The proposed solution contributes a flexible, cost-effective strategy for immersive workforce training aligned with the principles of Industry 5.0.
Multimedia AI now characterizes multiple concurrent streams at high resolutions, spanning video, audio, language, and multimodal fusion. Energy, bandwidth, and even water for cooling have become first-order constraints for real deployments. Training a single large NLP model has been estimated to emit up to 284 tons of CO2, while infrastructure and model choices can shift emissions by orders of magnitude; at the same time, data centers consume huge volumes of water for cooling (hundreds of thousands). In this position paper, we argue that we cannot continue to choose models based on accuracy and throughput alone. We propose a Sustainability Card for Multimedia AI-Correct Outputs per kWh, Joules per Sample, Bytes per Output, and optional Water per 1,000 Outputs-and outline a simple measurement protocol.
We present "Dialogue-Pseudo", a dialogue-aware pseudonymization framework that (i) maps each real speaker to a distinct pseudo-speaker via a dialogue-aware PartitionAssignment-Selection procedure to preserve inter-speaker separability, (ii) replaces personally identifiable information with syllable- and accent-matched surrogates to maintain phonorhythmic timing, and (iii) applies bounded prosody control that normalizes global pitch/energy while preserving relative contours and turn-taking cues. Experiments on the RWCP conversational corpus with a pseudo-speaker pool derived from Common Voice show improved privacy under an ECAPA-TDNN speaker verification model: Equal Error Rate (EER) increases from 37.38% (original) to 42.22% (pseudonymized). Prosody-oriented statistics (F0 mean/variance, RMS energy, pause ratio, turn length) remain close across conditions, and UMAP visualizations indicate that pseudo-speakers are well separated from both the sources and from one another. These results support Dialogue-Pseudo as a practical approach to privacy-preserving dialogue analytics that extends pseudonymization beyond timbre conversion to dialogue-relevant linguistic and prosodic cues, thereby balancing unlinkability with the retention of task-critical information.
